Digital Inclusive Finance, Human Capital and Inclusive Green Development—Evidence from China

Abstract

To analyze the impact of digital inclusive finance and human capital on inclusive green economic development in China, we build a comprehensive indicator system to measure the level of inclusive green development and use the super-efficiency SBM method to measure the inclusive green total factor productivity (IGTFP) in Chinese cities, then the system GMM model is used to empirically test the direct and interactive influences. Inclusive green development in China has maintained a growing trend in recent years, reaching a peak in 2017. The development of digital inclusive finance in terms of breadth, depth and degree of digitization is conducive to promoting inclusive green development. Although human capital does not directly affect inclusive green development, it plays a significantly positive moderating role in the process of digital inclusive finance promoting inclusive green development. In this paper, the impact of digital inclusive financial and human capital and their interactions on inclusive green development is analyzed within a unified framework, which has important practical significance for the orderly promotion of the development of digital inclusive finance, improving residents’ education level and promoting inclusive green development.

1. Introduction

Common prosperity is the characteristic of socialist modernization, and inclusive development (this paper uses the term inclusive development; however, it is also known as inclusive growth elsewhere) is the mode chosen to realize common prosperity. Since the Asian Development Bank (ADB) formally proposed the basic concept of “inclusive development” in 2007, Chinese scholars have conducted extensive discussions on inclusiveness and China’s practice. On 29 July 2014, the Political Bureau of the CPC Central Committee proposed that “development must be scientific development following the laws of economy, sustainable development by the laws of nature, and inclusive development under the laws of society”, which was the first time that the idea of inclusive development appeared in top-level policy design. This fully demonstrates the importance of inclusive development in China’s economic development and structural transformation. Inclusive green development not only focuses on “efficiency” and “equity” but also considers “ecology” as well. The realization of this proposition requires policy adjustments and coordination in many aspects. Furthermore, since financial development is one of the most crucial parts of economic growth, the orderly development of finance to promote the inclusive green development of our economy and help to realize common prosperity has become an essential and urgent issue.

Since the reform and opening, the development of China’s finance has become an essential driving force of rapid economic growth. Especially with the development of the Internet, inclusive finance was integrated with digital technology in China, and digital inclusive finance has achieved rapid development. Digital inclusive finance reduces the dependence on traditional financial outlets. With the help of the Internet, finance can be spread to remote areas and benefit more vulnerable groups, which reflects the basic principle of being people-oriented. Theoretically, it is consistent with the direction of inclusive development in China. However, due to regional differences in the development process of digital inclusive finance, it is worth further discussing whether the development of digital inclusive finance has promoted inclusive green development.

On the other hand, the improvement of residents’ education level can effectively promote social equity and adjust social distribution. Investment in basic education helps residents in rural and remote areas improve their competitiveness in reaping the benefits from human capital investment and helps narrow the urban-rural gap and regional imbalance. Investment in higher education accelerates economic development by enhancing innovation and technological capabilities. The development of digital inclusive finance depends on residents’ financial literacy, which is positively correlated with residents’ education level. Therefore, the improvement of education level is conducive to the development of digital inclusive finance and may also play a positive moderating role in the process of digital inclusive finance promoting inclusive green development. Therefore, this paper plans to further empirically test whether human capital will indirectly affect inclusive green development through interaction with digital inclusive finance, in addition to the direct impact of human capital on the inclusive green development of the economy.

Based on China’s city-level data from 2011 to 2019, this paper first constructs a comprehensive evaluation index system of IGTFP and uses the super-efficiency SBM model to calculate it as a measure index of inclusive green development. Then, the GMM dynamic panel model is used to empirically analyze the impact of digital inclusive finance on the inclusive green development of China’s economy and the effect of the interaction between digital inclusive finance and human capital. The main contributions of this paper are as follows. Firstly, we constructed IGTFP as the measurement of inclusive green development from the perspective of ecological construction and income equity in inclusive green development, which expanded the measurement method and system of inclusive green development and enriched its connotation. Secondly, this paper innovatively puts forward the effect of the interaction between digital inclusive finance and human capital on inclusive green development and provides a new perspective for related study on inclusive green development, suggesting that the research regarding the influencing factors of inclusive green development should be further perfected, so as to make policy proposals that are more comprehensive and effective. Thirdly, this paper adopts 2011–2019 city-level data for panel data analysis, which increases the amount of data and has more universality and stability compared with most studies that adopt provincial data.

Since the Asian Development Bank put forward the concept of inclusive development in Strategy 2020 in 2008, the academic community has not formed a unified definition of inclusive development. Regarding the theoretical connotation and policy significance of inclusive development, some scholars believe that the essence of inclusive development is to reduce the income gap [1,2,3]. Some scholars believe that inclusive development should include income and welfare growth [4,5] and pay attention to the varying opportunities to acquire welfare between different income groups [6,7]. On this basis, some scholars have further incorporated “green” into the inclusive development system. Some scholars, based on welfare economics, believe that inclusive development needs to pay attention to the welfare growth and intergenerational inheritance of current and future generations when weighing the relationship between economic growth, inclusiveness and green [8,9]. Other scholars believe that “inclusive development is an economic growth pattern that aims to reduce regional differences while taking into account ecological and environmental protection and providing more opportunities for poor areas and people” [10].

There are two main methods to measure inclusive development in quantitative research. One is to depict the opportunity curve [11], while the other is to construct an indicator system including economic growth, equality of opportunity and green development [12,13]. However, the above methods have some limitations. Inclusiveness described by the opportunity curve is usually limited to a specific field, such as education and medical, and cannot comprehensively measure the overall inclusiveness of a country or region. Although the construction of a comprehensive indicator system can reflect the general inclusiveness, it involves the weight construction of each indicator, which is highly subjective and difficult to reflect the differences between groups.

Total factor productivity is an important engine of economic growth. Due to the limited resources and increasingly serious environmental pollution, the concepts of green development and sustainable development have attracted people’s attention. Resources and the environment are not only endogenous variables that affect economic development, but also rigid constraints that limit the quality of economic development [14,15]. Compared with the total factor productivity that only considers the expected output, the green total factor productivity that incorporates unexpected output, such as pollutant emissions, into the indicator system is more comprehensive and objective. Therefore, some scholars began to use resource consumption and environmental pollution as measurement indicators and incorporated them into the calculation system of total factor productivity [16,17]. The total factor productivity obtained in this way is called green total factor productivity. On this basis, some scholars have begun to consider the inclusiveness of GTFP [18,19]. They believe that the government should pay attention to environmental protection and benefit equity of economic achievements while increasing the TFP; therefore, IGTFP was proposed.

In general, the existing measurement methods for inclusive green development, whether through depicting the opportunity function or building a comprehensive index system, are all essentially measured by a current indicator. It is difficult to judge whether its economic growth is an extensive development with high input and high output or an efficient development with low input and high output. Using super-efficiency SBM to measure inclusive green development by measuring total factor productivity, we can observe whether China’s economic operation maintains efficient development. We can also analyze whether “inclusive” and “green” are achieved at the same time as economic growth.

Through sorting out relevant studies on inclusive development, it is found that although there is no unified concept of inclusive development at present, its connotation mainly includes economic growth, ecological and environmental protection and social inclusiveness [20,21]. It can be considered that inclusive development is an economic development model that takes economic growth as its goal and considers ecological and environmental protection and equal social opportunities. In terms of quantitative research, whether describing the opportunity function or constructing a comprehensive index system, the comprehensive index of inclusive development that is finally obtained only measures the “inclusiveness” and “greenness” of economic growth from the perspective of the current period. In this paper, inclusive development from the standpoint of total factor productivity is explored to observe whether China’s economic operation can maintain efficient growth; additionally, it can be used to analyze whether economic growth has achieved the status of being “inclusive” and “green”.

Studies on the relationship between financial development and income distribution start from the GJ model proposed by Greenwood et al. [22], who pointed out that the relationship between financial development and income distribution generally presents an inverted U-shaped trajectory. Banerjee et al. [23], Galor et al. [24], Aghion et al. [25], Mookherjee et al. [26] and other studies have proposed that financial development can improve capital allocation efficiency and income distribution patterns by enabling low-income groups to obtain more financial services. In recent years, scholars at home and abroad have conducted many empirical tests on the relationship between financial development and social equity. For example, Burgess et al. [27] found that increasing deposit and loan issuance and expanding outlets in rural financial service gaps helped reduce poverty. Bae et al. found that the imperfect financial system makes it difficult for low-income people to afford transaction costs and that improving the availability of financial services is the key to promoting income equity [28]. In addition, since poverty in China is mainly concentrated in rural areas, Chinese scholars have carried out more discussions on the impact of rural financial development on poverty alleviation. For example, Ding et al. analyzed the depth and breadth of financial development and the efficiency of financial institutions and found that the proportion of loan outlets and the number of legal persons in rural financial institutions could significantly reduce the urban-rural income gap [29]. Wang et al. [30] constructed a comprehensive measure of the development level of rural financial inclusion, proving that rural financial inclusion has a significantly negative impact on the urban-rural income gap.

The concept of inclusive finance was first used by the United Nations for the promotion of the “2005 International Year of Microfinance”, and later promoted by the United Nations and the World Bank. The United Nations defines inclusive finance as a financial system that provides practical and comprehensive services to all social strata and groups. At present, inclusive finance covers a variety of financial products and services, such as savings, payment, insurance, financial management and credit [31]. Digital inclusive finance could be intuitively understood as lowering the threshold of financial services through digital technology to promote inclusive finance’s efficient and sustainable development. China has gradually integrated big data, blockchain and other technologies into the inclusive finance business and constantly improved credit investigation, laws and supportive policies in financial services to promote the healthy development of digital inclusive finance [32].

Existing studies show that digital inclusive finance positively impacts economic growth and poverty reduction. For example, Kapoor found that digital finance can promote economic growth [33]. Manyika et al. found that digital inclusive finance can solve the financing difficulties of small enterprises and promote the economic development of middle-income countries [34]. Patrick [35] confirmed that digital inclusive finance has a poverty reduction effect. Zhang et al. proved that digital inclusive finance could narrow the urban-rural income gap based on China’s provincial panel data [36]. Ren et al. conducted a sample survey of rural residents in Beijing, Tianjin and Hebei in China and confirmed that the availability of digital financial services significantly promoted rural inclusive development. In addition, in terms of environmental protection [37], Antoine et al. empirically found that digital inclusive finance can indirectly enhance regional ecological protection by promoting green economic growth [38].

Under the new normal of China’s economic development, promoting equity in public education and optimizing the allocation of educational resources at all levels are undoubtedly effective means to promote human capital accumulation, increase income, reduce poverty and promote high-quality economic development. Ramos et al. found that the increase in public education investment was conducive to alleviating income inequality [39]. Ali found that the increase in human capital stock significantly promoted inclusive development [40]. The development of digital inclusive finance should not ignore the impact of residents’ financial literacy, which is positively correlated with education level [41]. However, few studies have examined the interaction between financial development and education level or human capital accumulation in a unified framework and verified its impact on inclusive development.

Through the comprehensive analysis of existing studies, it is easy to see the following three characteristics: (1) although the ideas of inclusive development and digital inclusive finance are consistent, that is, they both pursue harmonious economic and social development, there is still much research to be conducted on the relationship between the two. (2) Existing studies have focused on the impact of digital inclusive finance on urban and rural income levels, which is merely the connotation of the “social equity” of inclusive development but ignores the connotation of “ecological and environmental protection”. In addition, most existing studies have ignored the interaction between financial development and human capital accumulation in promoting inclusive development. (3) Regarding research samples, most existing studies are based on provincial data or only analyze local areas, so there is hardly any research on digital inclusive finance development and inclusive green development at the level of prefecture-level cities.

In view of these, IGTFP is firstly constructed in this paper to reflect the level of inclusive green development which takes economic growth, social equity and ecological protection into account. Then, based on the panel data of 282 prefecture-level cities in China from 2011 to 2019, we examine the impact of digital inclusive finance and human capital development on inclusive green development and further analyze the interaction between digital inclusive finance and human capital, providing a more comprehensive perspective for research and analysis in related fields.

2. Materials and Methods

2.1. Construction of IGTFP

The core of inclusive green development is to reduce the gap between the rich and the poor while achieving green economic development and, thus, achieving balanced growth and common prosperity. Therefore, the measurement of IGTFP should be based on the measurement system of total factor productivity, and additionally includes the “three wastes of industry” and the urban-rural income ratio which hinder the “greening” and “inclusiveness”. Based on the connotation of inclusive green development, this paper constructed an indicator system to measure IGTFP (

Table 1. Input-output system and interpretation of IGTFP.

Indicator Type	Indicator		Measurement	Unit
Input	Labor		Total number of employees	Ten thousand people
	Capital		Fixed-asset investment	Ten thousand CNY
	Technology		Public service expenditure	Billion CNY
			Education expenditure	Billion CNY
			Scientific expenditure	Billion CNY
	Resource	Land Resource	Industrial and agricultural land	Ten thousand hectares
		Energy	Electricity consumption	Billion kilowatts
		Water resource	Total water supply	Billion cubic meters
Output	Expected output		GDP	Ten thousand CNY
	Unexpected output		Industrial waste water emissions	Billion tons
			Industrial waste gas emissions	Billion cubic meters
			Industrial solid waste emissions	Ten thousand tons
			Urban–rural income ratio	%

). As for the input factors, this paper refers to the literature of Sun et al. [42] and includes labor, capital, technology and resources as input factors. In terms of output, this paper refers to the literature of Sun et al. [42], Li et al. [19] and Li et al. [43] and includes the income and consumption levels of urban and rural residents, in addition to GDP, into the expected output, while non-expected output includes the “three wastes of industry” and urban-rural income ratio.

2.2. Measurement Method of IGTFP

Based on the concept of relative efficiency, data envelopment analysis can evaluate the relative effectiveness of decision-making units (DMUs) with the help of mathematical programming and statistical data. However, traditional non-parametric data envelopment analysis measures efficiency from radial direction and angle, and lacks consideration of the relaxation of input-output, so it is not easy to ensure the accuracy of the efficiency value obtained. However, the SBM model and super-efficiency DEA can directly put the “relaxation” variables into the objective function and can sort and distinguish multiple decision units. In view of this, under the assumption of variable returns to scale, this paper attempts to analyze the development level of China’s economic efficiency in the new period by using the super-efficiency SBM model containing unexpected outputs.

Since the traditional data envelopment model cannot distinguish whether multiple DMUs are effective or not, the super-efficiency SBM introduces relaxation variables. Here, the production frontier of invalid DMUs does not change, so its efficiency value is the same as that of the general SBM. Suppose there are n DMUs, each of which has m inputs, q₁ expected outputs and q₂ unexpected outputs. The expression of super-efficiency SBM containing unexpected outputs is as follows:

$$\min \delta =\frac{1+\frac{1}{m}{\displaystyle \sum _{i=1}^m\frac{s_i{}^{-}}{x_{ik}}}}{1-\frac{1}{q_1+q_2}({\displaystyle \sum _{r=1}^{q_1}\frac{s_r{}^{+}}{y_{rk}}}+{\displaystyle \sum _{t=1}^{q_2}\frac{s_t{}^{b-}}{b_{tk}}})}$$

(1)

$$s.t.\{\begin{array}{l}{\displaystyle \sum _{i=1,j\ne k}^n{x}_j{\lambda }_j-s_i{}^{-}\le x_{jk}}\\ {\displaystyle \sum _{i=1,j\ne k}^n{y}_j{\lambda }_j+s_r{}^{+}\ge y_{rk}}\\ {\displaystyle \sum _{i=1,j\ne k}^n{b}_j{\lambda }_j-s_t{}^{b-}\le b_{tk}}\\ 1-\frac{1}{q_1+q_2}({\displaystyle \sum _{r=1}^{q_1}\frac{s_r{}^{+}}{y_{rk}}}+{\displaystyle \sum _{t=1}^{q_2}\frac{s_t{}^{b-}}{b_{tk}}})>0\\ {\lambda }_j,s_i{}^{-},s_r{}^{+},s_t{}^{b-}\ge 0\\ i=1,2,\dots ,\hspace{1em}m;r=1,2,\dots ,\hspace{1em}{q}_1;\\ t=1,2,\dots ,\hspace{1em}{q}_2;j=1,2,\dots ,\hspace{1em}n(j\ne k)\\ \end{array}$$

(2)

In Equations (1) and (2), $\delta$ is the efficiency value, $j$ is the decision-making unit, ${\lambda }_j$ is the intensity variable, $s$ represents the relaxation variable of each variable, $s_i$ represents the relaxation variable of input, $s_r$ and $s_t$ are relaxation variables of expected output and unexpected output, respectively. $x$ represents input, for example, $x_{ik}$ is the input item $i$ of the kth decision unit. $y$ and $b$ represent expected output and unexpected output, respectively. $y_{rk}$ represents the expected output item $r$ of the $k\mathrm{th}$ decision unit and $b_{tk}$ represents the unexpected output of the $t\mathrm{th}$ term of the $k\mathrm{th}$ decision unit.

3. The Impact of Digital Inclusive Finance on Inclusive Green Development

3.1. Model Setting

Because of the extensive persistence of economic and social phenomena, we introduce the first- and second-order lag terms of the explained variable (inclusive green total factor productivity, IGML) to construct a dynamic analysis model, so as to analyze the influence of other factors on inclusive green development under the active control of the inertial effect. In addition, the traditional OLS or static fixed effect model cannot solve the simultaneous endogeneity problem between independent and dependent variables. Therefore, this paper selects the System Generalized Moment Estimation method (SYS-GMM) for dynamic panel regression and tests the assumption of no autocorrelation between residuals and the validity of instrumental variables.

Firstly, variables regarding digital inclusive finance are introduced to analyze their impact on the inclusive green development of China’s economy. The regression model is shown in Equation (3).

$${\mathrm{IGML}}_{it}=\alpha +{\rho }_1{\mathrm{IGML}}_{i,t-1}+{\rho }_2{\mathrm{IGML}}_{i,t-2}+{\beta }_1{\mathrm{fd}}_{it}+{\beta }_2{\mathrm{stu}}_{it}+{\beta }_3{Z}_{it}+u_i+{\epsilon }_{it}$$

(3)

where ${\mathrm{IGML}}_{it}$ represents the IGTFP of city $i$ in the year $t\left(t=2011,2012,\cdots ,2019\right)$; ${\mathrm{IGML}}_{i,t-1}$, ${\mathrm{IGML}}_{i,t-2}$ represent the first- and second-order lag terms of the explained variable, respectively; ${\mathrm{fd}}_{it}$ represents the development index of digital inclusive finance, ${\mathrm{stu}}_{it}$ represents human capital, and $Z_{it}$ are the control variables. $u_i$ is the individual effect of each city, which represents the factors that cannot be identified and do not change over time but have an impact on inclusive green development; ${\epsilon }_{it}$ denotes the random error term containing other influencing factors.

In addition, in order to analyze the impact of human capital on inclusive green development and the interaction between digital inclusive finance development and human capital, the above regression equation is not enough to meet the requirements. Therefore, the interaction term of financial development and human capital is introduced based on Equation (3). To verify whether human capital plays a positive moderating role in the process of digital inclusive finance promoting inclusive green development, the regression model adopted is shown in Equation (4).

$$\begin{array}{c}{\mathrm{IGML}}_{it}=\alpha +{\rho }_1{\mathrm{IGML}}_{i,t-1}+{\rho }_2{\mathrm{IGML}}_{i,t-2}+{\beta }_1{\mathrm{fd}}_{it}+{\beta }_2{\mathrm{stu}}_{it}+{\beta }_3{Z}_{it}\\ +{\beta }_4{\mathrm{fd}}_{it}\times {\mathrm{stu}}_{it}+u_i+{\epsilon }_{it}\hfill \end{array}$$

(4)

where ${\mathrm{fd}}_{it}\times {\mathrm{stu}}_{it}$ represents the interaction term between the development index of digital inclusive finance and human capital, and other variables are consistent with Equation (3). In Equation (3), if ${\beta }_1$ is significantly greater than 0, it indicates that the development of digital inclusive finance has a significantly positive impact on inclusive green development; if ${\beta }_2$ is significantly greater than 0, it indicates that the improvement of human capital level has a significantly positive impact on inclusive green development. In Equation (4), if ${\beta }_4$ is significantly greater than 0, it indicates that human capital has a significantly positive moderating effect in digital inclusive finance promoting inclusive green development. However, if the regression coefficient of human capital is insignificant or significantly negative, but the interaction term between human capital and the development index of digital inclusive finance is significantly greater than 0, it indicates that human capital has no direct contribution to inclusive green development but can play a positive role in inclusive green development via digital inclusive finance.

In addition, when using the system GMM model, it is necessary to test the validity of the instrumental variables and the assumption that the error term has no autocorrelation. In this paper, the Sargan test and AR (2) test are, respectively, adopted. If both p values are larger than the significance level, the model passes the test, indicating that the instrumental variables are valid and the model is set correctly.

3.2. Data Sources and Index Selection

In order to verify the theoretical mechanism, this paper adopts the panel data of 282 cities from 2011 to 2019 for the empirical test. Variables used in the model construction are described as follows. Firstly, IGTFP measured above is adopted as the explained variable to measure inclusive green development. Secondly, this paper mainly concerns the impact of the development of digital inclusive finance and human capital on inclusive green development. In terms of the measurement of digital inclusive finance, this paper adopts the total development index (fd) and three indicators of digital inclusive finance, namely, the breadth of coverage (cvg), the depth of use (dep) and the degree of digitization (dig). Moreover, these variables are transformed into the logarithmic scale. In measuring human capital, this paper adopts the number of college students per million (stu). Thirdly, considering data availability, this paper selects control variables from five aspects: urbanization degree, openness degree, economic development level, industrial structure and government expenditure. The degree of urbanization (urb) is measured by the proportion of total urban residents that are permanent urban residents. The degree of openness (trade) is measured by the total amount of import and export trade (in ten thousand CNY)/gross regional product (in hundred million CNY). The gross regional product measures the level of economic development (gdp). Industrial structure (ind) is measured by the proportion of the sum of the added value of China’s secondary and tertiary industries in GDP. Government expenditure (gov) is the expenditure of government finance. The data of control variables is obtained from each city’s statistical yearbook from 2011 to 2019, and the random forest multiple imputations is used to interpolate the missing data. Variable names and their measurement methods are shown in

Table 2. Variables and instructions.

Variable Type	Variable Name		Measurement Method	Unit
Dependent Variable	IGTFP	IGML	See above	--
Independent variable	Total development index of digital inclusive finance	fd	Development of digital inclusive finance	--
	The breadth of coverage of digital inclusive finance	cvg	Digital financial account coverage	--
	Depth of use of digital inclusive finance	dep	The actual use of digital financial services	--
	Degree of digitization of digital inclusive finance	dig	The degree of digitization and credit of digital inclusive finance	--
	Human capital	stu	Number of college students per million	%
Control variables	Degree of urbanization	urb	Urban resident population/urban population	%
	Degree of openness	trade	Total amount of import and export trade/GDP	%
	Level of economic development	gdp	GDP	100 billion CNY
	Industrial structure	ind	The sum of added value of secondary and tertiary industries/gross regional product	%
	Government expenditure	gov	Financial expenditure of local government	10 billion CNY

.

4. Result

4.1. The Temporal Trend of IGTFP

In this paper, 293 prefecture-level cities in China are taken as the research objects, and the cities with severe data missing are removed. The final sample number of cities is 282, with data sources from the China Statistical Yearbook, China Energy Statistical Yearbook, China Urban Statistical Yearbook, local municipal statistical yearbooks and China Economic Network.

This paper takes the proportion of urban GDP in the total urban GDP as the weight and obtains the averaged IGTFP at the national level from 2011 to 2019 (in Figure 1). IGTFP averaged 1.189 from 2011 to 2019, with an average annual growth rate of 1.3%. Prefecture-level cities in China have generally achieved an apparent growth in inclusive green development during the 12th and 13th Five-Year Plans. In terms of time, inclusive green development has maintained steady growth during the 12th Five-Year Plan Period (2011–2015). IGTFP made a significant leap in the first two years of the 13th Five-Year Plan (2016–2017), reaching a peak in 2017. Since then, the growth rate of inclusive green development dropped slightly in 2018 and 2019, but it still maintains considerable growth momentum.

4.2. Impact of the Development of Digital Inclusive Finance on Inclusive Green Development

Table 3. Impact of digital inclusive finance development on inclusive green development.

Independent Variables	Explained Variable: IGTFP
	(4.1)	(4.2)	(4.3)	(4.4)
IGML (−1)	−0.243 ***	−0.217 ***	−0.260 ***	−0.219 ***
IGML (−2)	−0.122 ***	−0.101 ***	−0.124 ***	−0.084 ***
fd	0.551 ***
cvg		0.489 ***
dep			0.433 ***
dig				0.231 ***
stu	−0.003	−0.007	0.002	−0.002
urb	−0.359 ***	−0.341 ***	−0.335 ***	−0.131
trade	0.035	0.033	0.030	0.042
gdp	0.041 ***	0.040 ***	0.041 ***	0.045 ***
ind	−0.509 ***	−0.630 ***	−0.455 ***	−0.389 **
gov	−0.020 ***	−0.020 ***	−0.020 ***	−0.020 ***
constant	−0.634 **	−0.220	−0.036	0.681 ***

Note: “***” and “**” mean significance at 0.01 and 0.05 levels, respectively.

shows the regression results of the system GMM model. The p values of AR (2) and the Sargan test are both higher than the significance level (0.05), indicating that the model is set correctly, and the instrumental variables are valid. The total development index of finance (fd) is included in the model (4.1) as the core explanatory variable concerning financial development, so as to study the impact of the overall development level of digital inclusive finance on inclusive green development. In order to further study the impact of different aspects of digital inclusive finance development, model (4.2), (4.3) and (4.4) include the breadth of coverage (cvg), depth of use (dep) and degree of digitization (dig), respectively, as the core independent variables concerning financial development.

In the four models, the coefficients of the first- and second-order lag terms of inclusive green development are both significantly negative. Since IGTFP is used as the explained variable in this paper, the higher the growth rates in the several previous periods and the larger the base of inclusive green development in the current period, the lower the growth rate in the current period. Therefore, such results are in line with expectations. The coefficient of the total financial development index (fd) in the model (4.1) is significantly positive, indicating that inclusive financial development is conducive to accelerating inclusive green development. In fact, the development of digital inclusive finance is conducive to reducing the cost of financial services, enabling more residents, especially the middle- and low-income groups, to access financial services and obtain benefits through the optimal allocation of personal assets. In addition, digital inclusive finance can also provide financial support for small- and medium-sized enterprises to purchase new energy vehicles and conduct sewage reform and other activities to promote ecological protection. The coefficient of human capital (stu) is insignificant, indicating that the development of education level has no apparent direct effect on further accelerating inclusive green development. In models (4.2)–(4.4), the coverage of digital inclusive finance (cvg), the depth of use (dep) and the degree of digitization (dig) are all significantly positive, indicating that the development of digital inclusive finance in these three aspects has a significant role in promoting inclusive green development.

As for control variables, the coefficient of the gross regional product (gdp) is significantly positive, indicating that economic growth is conducive to accelerating inclusive green development. In fact, economic growth plays an irreplaceable role in maintaining people’s income level, reducing the size of the poor group and the gap between the rich and the poor, and supporting the development of specific industries (such as environmental protection). The coefficient of the industrial structure (ind) is significantly negative, which highlights that in order to promote the development of secondary and tertiary industries, a large amount of capital input and rural labor transfer effectively promoted the development of the urban economy but ignored the coordinated progress of the agricultural sector, thus, widening the urban-rural income gap. The coefficient of government expenditure (gov) is significantly negative, which confirms the research results of Meng et al. [44]. It points out that, although increasing capital and productive expenditure of the government will promote economic growth, nonproductive expenditure will restrain economic growth by crowding out household consumption and occupying other fiscal expenditures. At the same time, the government expenditure on infrastructure construction has a strong lag effect on economic growth and it negatively impacts the current period. The coefficient of urbanization degree (urb) is significantly negative. Urbanization tends to simply transfer the rural labor force to cities but ignores economic and social urbanization. Farhana [45] also pointed out that when the urbanization process reaches a high level, developing countries are still in the stage of extensive economic growth heavily dependent on traditional manufacturing, with heavy environmental load and low innovation capacity, which will bring resistance to inclusive green development in a certain period. The coefficient of opening level (trade) is not significant. However, previous studies have shown that increasing the degree of opening to the outside world is conducive to improving the production and living conditions of residents and promoting inclusive green development by taking advantage of domestic and foreign markets, resources and technologies [46,47,48]. Despite that, our results further show that opening up has no significant effect on further accelerating the speed of inclusive green development, which is consistent with China’s current new economic development pattern of “taking major domestic cycle as the main body” and “promoting double cycle by internal cycle”.

4.3. Impact of Human Capital and Digital Inclusive Finance on Inclusive Green Development

In order to further analyze the indirect role of human capital in promoting inclusive green development, the interaction between human capital and financial development indices is introduced into the above four models to analyze its moderating effect. The regression results are shown in

Table 4. Impact of human capital and digital inclusive finance development on inclusive green development.

Independent Variables	Explained Variable: IGTFP
	(4.5)	(4.6)	(4.7)	(4.8)
IGML (−1)	−0.246 ***	−0.221 ***	−0.263 ***	−0.219 ***
IGML (−2)	−0.122 ***	−0.103 ***	−0.123 ***	−0.083 ***
fd	0.489 ***
fd × stu	0.039 **
cvg		0.443 ***
cvg × stu		0.036 *
dep			0.371 ***
dep × stu			0.038 ***
dig				0.204 ***
dig × stu				0.013
stu	−0.216 **	−0.200 ***	−0.204 ***	0.013
urb	−0.350 ***	−0.333 ***	−0.329 ***	−0.129
trade	0.037	0.035	0.034	0.043 *
gdp	0.041 ***	0.040 ***	0.040 ***	0.045 ***
ind	−0.485 ***	−0.601 ***	−0.431 ***	−0.390 ***
gov	−0.020 ***	−0.020 ***	−0.020 ***	−0.020 ***
constant	−0.325	0.005	0.269	0.830 ***

Note: “***”, “**” and “*” mean significance at 0.01, 0.05 and 0.1 levels, respectively.

. The model passed AR (2) and Sargan tests; therefore, the regression results are valid.

It can be seen that the interaction term (fd × stu) of human capital and total financial development index in the model (4.5) is significantly positive, indicating that although human capital growth has no significant direct effect on promoting inclusive green development, it can still play some indirect roles. For example, education development is conducive to improving residents’ financial literacy and creates a better development environment for digital inclusive finance. At the same time, more highly educated talents will help the development of digital inclusive finance, thus, playing a positive moderating effect. It is further found from the model (4.6)–(4.8) that the interaction terms between human capital and both the depth of use and coverage breadth of digital inclusive finance are significantly positive. However, the interaction with the degree of digitization is insignificant, indicating that digital inclusive finance can better promote inclusive green development with the help of human capital by increasing coverage and depth of use. The other regression results are consistent with Table 3.

4.4. Heterogeneity Analysis

In consideration of the differences in regional development, based on model (3), we re-divide 282 city samples into eastern, central and western regions, and repeat regression analysis, respectively, to verify whether the above conclusions apply to the different areas. The regression results are shown in

Table 5. Impact of digital inclusive finance development on inclusive green development in different regions.

Independent Variables	Explained Variable: IGTFP
	Eastern Region	Central Region	Western Region
IGML (−1)	−0.263 ***	−0.195 ***	−0.206 ***
IGML (−2)	−0.129 ***	−0.178 ***	−0.015
fd	0.604 ***	0.535 ***	0.319 **
stu	−0.024 *	−0.007	0.0003
urb	−0.667 ***	−0.039	0.025
trade	0.108	−0.095	0.025 **
gdp	0.059 ***	0.045 **	−0.002
ind	−0.331	−0.814 ***	−0.628
gov	−0.027 ***	−0.015 **	−0.001
constant	−1.537 **	−0.436	0.356

Note: “***”, “**” and “*” mean significance at 0.01, 0.05 and 0.1 levels, respectively.

. The coefficient of $\mathrm{fd}$ is significantly positive in the models of the three regions, indicating that the development of digital inclusive finance will be conducive to inclusive green economic development, improve the economic and living conditions of vulnerable groups and promote ecological construction no matter in which region of China. In terms of control variables, urbanization degree (urb) has a significantly negative effect on inclusive green development in eastern China. Industrial structure (ind) plays a significantly positive role in inclusive green development in central China. Foreign trade (trade) has significantly promoted inclusive green development in the western region. Therefore, the influencing factors of inclusive green development in different regions are not the same, and more targeted regional policies can promote inclusive green development.

4.5. Robustness Test

To further test the robustness of the regression results among digital inclusive finance development, human capital and inclusive green development, we use the differential GMM method to repeat the regression analysis. The relationship between digital inclusive finance development, human capital and inclusive green development has not changed substantially. The sign and significance of the control variables’ regression coefficients are consistent with that of the system GMM, indicating that the conclusions obtained in this paper are robust and reliable.

5. Conclusions and Discussion

Based on the panel data of 282 prefecture-level cities from 2011 to 2019, this paper first uses the super-efficiency SBM method to measure IGTFP to measure the inclusive green development of each city. Then, the system GMM model is adopted to empirically test the role of digital inclusive finance and human capital development in promoting inclusive green development. The results show that:

(1)

The prefecture-level cities in China have maintained stable inclusive green development, in general, in the past decade. IGTFP reached an all-time high in 2017 and declined slightly in 2018 and 2019, but still showed a steady growth trend.

(2)

Digital inclusive finance has a significant positive impact on promoting inclusive green development and its coverage, depth of use and degree of digitization all display a positive impact. In addition, the heterogeneity test shows that the development of digital inclusive finance plays a significant role in promoting inclusive green development in the eastern, western and central regions of China.

(3)

Human capital has no significant direct contribution to accelerating inclusive green development; however, human capital plays a significantly positive moderating role in the process of digital inclusive finance promoting inclusive green development, and this moderating role is mainly reflected in the interaction with the coverage and depth of use of digital inclusive finance.

Based on the above findings and conclusions, this paper draws the following policy implications:

Firstly, China should further promote inclusive green development as IGTFP has dropped slightly in the past two years. In promoting economic growth, we should consider social equity and ecological protection, for example, by improving the social security system, strengthening the awareness of the environmental protection of residents and enterprises, and promoting the development of a new energy industry, so as to achieve high-quality economic growth and build a community with a shared future for humankind under the guidance of inclusive green development. Cities with low IGTFP can develop rural tourism, e-commerce and other related industries according to local characteristics, and form an economic form with low input, high output and lower income gap with business models such as “New Economy” and “Digital Economy”. While promoting rural revitalization, the “Digital Economy” can also reduce the income gap between urban and rural areas, which can not only promote the improvement of the IGTFP level but also achieve China’s common prosperity goal.

Secondly, the government should make full use of the positive role of digital inclusive finance in promoting inclusive green development, strengthen the overall plan for developing digital inclusive finance, perfect laws and regulations and improve market openness and transparency. At the same time, governments at all levels should expand their thinking and make bold innovations in popularizing financial knowledge, increasing the frequency of residents’ use of financial products and lowering the threshold of financial services. In particular, governments should pay attention to the construction and support of digital inclusive finance infrastructure in areas with large income gaps, promote the development of digital inclusive finance according to local conditions and further improve its coverage, depth of use and degree of digitization.

Thirdly, the development of digital inclusive finance largely depends on the financial cognitive level of financial managers and demanders, and education plays a significant moderating role. Therefore, governments at all levels should continue to promote equal access to education. Local governments should strengthen public services that can provide educational opportunities for low- and middle-income groups, and pay attention to improving their financial literacy, for example, through television, radio, Weibo and other media to popularize financial knowledge for residents, improving their financial decision-making ability. Financial institutions in central and western regions and rural areas should learn from the service and management models of developed regions to accelerate their own development of digital inclusive finance.

In addition, in China’s current economic development, fiscal expenditure has a certain degree of a hindrance to inclusive green development. Therefore, governments at all levels should adequately adjust the scale and structure of fiscal spending and gradually relax their economic functions, such as improving the efficiency of investment spending, reducing consumption spending and promoting independent consumption by residents and enterprises, so as to ensure coordination between fiscal spending policies and inclusive green development. In terms of urbanization and industrial upgrading, the extensive economic growth model that relies heavily on traditional manufacturing should be avoided, and rural economic development should be taken into account as rural labor transfers to cities. Economic growth plays an irreplaceable role in promoting inclusive green development and, therefore, it is necessary to fully unleash the potential of less developed areas, realize full economic development and improve people’s production and living conditions overall.